Revealing the role of the gut microbiota in enhancing targeted therapy efficacy for lung adenocarcinoma

Lung adenocarcinoma (LUAD) is the leading cause of cancer-related death globally. Although the gut microbiota's role in the antitumor efficacy of many cancers has been revealed, its involvement in the response to gefitinib therapy for LUAD remains unclear. To fill this gap, we conducted a longitudinal study that profiled gut microbiota changes in PC-9 tumor-bearing mice under different treatments, including gefitinib monotherapy and combination therapies with probiotics, antibiotics, or Traditional Chinese Medicine (TCM). Our findings demonstrated that combining probiotics or TCM with gefitinib therapy outperformed gefitinib monotherapy, as evidenced by tumor volume, body weight, and tumor marker tests. By contrast, antibiotic intervention suppressed the antitumor efficacy of gefitinib. Notably, the temporal changes in gut microbiota were strongly correlated with the different treatments, prompting us to investigate whether there is a causal relationship between gut microbiota and the antitumor efficacy of gefitinib using Mediation Analysis (MA). Finally, our research revealed that thirteen mediators (Amplicon Sequence Variants, ASVs) regulate the antitumor effect of gefitinib, regardless of treatment. Our study provides robust evidence supporting the gut microbiota's significant and potentially causal role in mediating gefitinib treatment efficacy in mice. Our findings shed light on a novel strategy for antitumor drug development by targeting the gut microbiota. Supplementary Information The online version contains supplementary material available at 10.1186/s40164-024-00478-7.

Experimental design and model construction A total of 34 mice were divided into six groups: healthy control (HC, mice without PC-9 incubation or any treatment, n=4), blank control (LUAD, tumor-bearing mice without any treatment, n=6), TKI (tumor-bearing mice treated with gefitinib alone, 32.5 mg/kg per day, n=6), TKI+TCM (tumor-bearing mice treated with gefitinib 32.5 mg/kg per day and Qilian mixture 10.4mL/kg 12h -1 , n=6), TKI+ANT (tumor-bearing mice treated with gefitinib 32.5 mg/kg per day and quadruple antibiotic 10 ml/kg 12h -1 , n=6), and TKIs + PRO (tumor-bearing mice treated with gefitinib 32.5 mg/kg per day and probiotic 0.2 g/kg 12h -1 , n=6).At baseline, approximately 5×10 6 PC9 cells were subcutaneously inoculated into the right flank of mice, except for healthy controls (HC).Daily oral gavage gefitinib and combination therapies were administrated seven days after tumor inoculation when the diameter of the tumor reached approximately 3 mm.Body weight and stool samples were measured and collected every seven days from baseline (day 0) to the end of the trial (day 35).The tumor volume (V, mm 3 ) was measured every seven days from day 7 to day 35, e.g., we measured the longest diameter (L, mm) and its vertical width (W, mm) of the tumor with a vernier caliper, and tumor volume was calculated by V = (L × W 2 )/2.In addition to the mice mentioned above, we also included 6 mice with TCM alone (Qilian mixture 10.4mL/kg 12h -1 ) as treatment for comparison.

Pathomorphological observation
After sacrificing the mice, the tumor body was taken, and the central necrotic tissue and surrounding connective tissue were stripped and fixed with 4% paraformaldehyde.Then, the tumor tissue was embedded in paraffin, and the pathological status was observed after hematoxylin-eosin (HE) staining.

Detection of KI-67 and Caspase-3
We first fixed the tissue specimens with 4% paraformaldehyde, followed by routine paraffin embedding, and then prepared tissue sections with a thickness of 4μm.After baking and deparaffinizing the tissue sections, we boiled them in an antigen retrieval solution with a pH of 6.The sections were then blocked using a solution containing 5% BSA and 1% goat serum and incubated overnight at 4°C with primary antibodies against KI-67 or Caspase-3.Subsequently, we incubated the tissue sections according to the instructions provided with the VECTASTAIN® Elite® ABC HRP Kit (PK-6100, VECTOR, USA) and ImmPACT® DAB Peroxidase (HRP) Substrate (SK-4105, VECTOR, USA).The stained tissue sections were then photographed under a microscope (×200 magnification).

Serum tumor marker test (ELISA)
At the end of the trial (day 35, after sample collection and measurement), the mice in each group were anesthetized, and their blood was collected.
Blood samples were left standing for 30 minutes at 3000 rpm for 10 minutes, and the supernatant (serum) was then stored in the -80 ℃ refrigerator.The serum levels of tumor markers (NSE, CEA, and CYP-19) were detected using the Mouse Neuron-specific enolase (NSE) ELISA Kit (BS-E9103M2 48T, JSBOSSEN, China), mouse carcinoembryonic antigen (CEA) ELISA Kit (BS-E8644M2 48T, JSBOSSEN, China), and mouse cytochrome P450-19 (CYP-19) ELISA Kit (BS-E19403M2 48T, JSBOSSEN, China), respectively, and analyzed according to the manufacturer's instructions.We then used an enzyme labeling analyzer (Thermo Fisher, US) to display the absorbance of NSE, CEA, and CYP-19 at 450 nm and used a standard curve to calculate their concentrations in the samples.
Fecal DNA extraction and 16S rRNA sequencing At days 0, 7, 14, 21, 28, and 35, fresh fecal samples of mice were directly collected from the anus, flash-frozen in liquid nitrogen for 1 min, and then transferred to a-80°C refrigerator.V3-V4 region (primers 341F/785R were used, and the target insert length was approximately 450bp) of 16S rDNA was amplified using the KAPA HiFi Hotstart PCR Kit (KAPA Biosystems) based on the manufacturer's protocol (KAPA Biosystems) and approximately 50 ng of extracted DNA per reaction.The thermocycling conditions were set at 95°C for 1 min, 55°C for 1 min, and 72°C for 1 min for 30 cycles, followed by a final extension at 72°C for 5 min.PCR reactions were performed in 50 ml triplicate and then combined.The MiniElute® Gel Extraction Kit (QIAGEN) was used to extract PCR products, which were then quantified with a NanoDrop ND-1000 spectrophotometer and a Qubit 2.0 Fluorometer (Thermo Electron Corporation).After purification, the amplicons were pooled in equimolar concentrations and their final concentration was determined using Qubit (Invitrogen).
16S rRNA sequencing data analysis Raw sequencing reads were demultiplexed and quality-filtered using the standard procedures of QIIME 2 (Bolyen et al., 2019(Bolyen et al., , version 2021.4) .4) with default parameters [3,4].The DADA2 (Callahan et al., 2016) plugin (version 2021.4.0) of QIIME2 [5] was used to denoise, de-replicate, and count amplicon sequence variants (ASVs), incorporating the following parameters: (1) forward and reverse reads were truncated to 150 bases; (2) forward and reverse reads with the number of expected errors higher than 2.0 were discarded; (3) chimeras were detected using the "consensus" method and removed.Taxonomies were assigned to the final sequences using the Silva database (v138-99) and classify-sklearn procedure.Chloroplastic and mitochondrial ASVs were removed.For other statistical analyses of the gut microbiota, Parallel-Meta 3.5 [6] was used to calculate and demonstrate beta diversity (PCoA) based on the ASVs.Differences in beta diversity based on Bray-Curtis dissimilarity were determined using non-parametric multivariate analysis of variance (PERMANOVA) with 999 random permutations.Data visualization in this study was performed using R (v4.0.2), e.g., the "ggplot2," "ggpubr" package were used for boxplot, scatter plot, and line chart, and the "pheatmap" was employed for the heatmaps.
To clearly illustrate the differences between treatments instead of time points, we visualized the differences in microbial composition between different treatments using Contextaware Tensor Factorization (CTF) [3,7].CTF is explicitly designed for metagenomic sequencing data and substantially utilizes the compositionality and sparsity of the data by finding a CP decomposition that best approximates non-zero values.

Mediation analysis
SparseMCMM is a high-dimensional and compositional aware method designed to estimate the causal mediation effect (ME) of microbiota using three factors (treatment, microbiome, and outcome).In particular, SparseMCMM used linear log-contrast regression and Dirichlet regression to model the causal mediation relationships of treatment, microbiome, covariates, and outcomes and provided a clear and sensible causal path among treatment, microbial composition, and outcome under four sufficient identifiable assumptions.
Here, we examined whether the overall ME of the microbiome community on the outcome was significant (OME test) or whether at least one component-wise ME was significantly non-zero (CME test).Specifically, we assigned the three factors required by the SparseMCMM: (1) we leveraged the samples from gefitinib alone (TKI) and any one of the combinational therapies (TKI+ANT, TKI+PRO, or TKI+TCM) to set the antibiotic, probiotic, or TCM as the treatment; (2) we used the longitudinal 16S rRNA data as the microbiome; and (3) we considered the antitumor measurements at the end of the trial (day 35) as the outcome.Significant results were derived at the ASV level, and only ASVs with a relative abundance higher than 0.1% and a prevalence higher than 30% were retained.P-values were obtained with 1000 permutations.

Supplemental Figures
Figure S1.Data collection scheme for stool samples and subject information.Rows represent individuals, and columns represent time points.Grey blocks refer to missing collection or failure in sequencing.A total of 34 mice were divided into six groups: Healthy control (HC, mice without PC-9 incubation or any treatment, 4 sequenced stool samples), Blank control (LUAD, tumor-bearing mice without any treatment, 6 sequenced stool samples), TKI (tumor-bearing mice treated with gefitinib alone, 6 sequenced stool samples), TKI+TCM (tumor-bearing mice treated with gefitinib and Qilian mixture, 6 sequenced stool samples), TKI+ANT (tumor-bearing mice treated with gefitinib and quadruple antibiotic, 6 sequenced stool samples), and TKIs + PRO (tumor-bearing mice treated with gefitinib and probiotic, 6 sequenced stool samples).Subject information (e.g., body weight, tumor volume, and marker test results) for most of the sequenced stool samples were collected.

Figure S2 .
Figure S2.HE staining of tumor tissues and expression KI-67 and Caspase-3 in each group at the end of the trial (day 35).The tumor cells were observed in all groups with diffuse and lamellar arrangement, slender fibrovascular interstitium, strong intercellular adhesion, round or round-like cells, eosinophilic cytoplasm, 1-2 centered nucleoli, and easy-to-see nuclear fission images, which proved the success of PC-9 tumor-bearing mouse model.Moreover, we found that the tumor cell necrosis was about 5% in the LUAD group with mild inflammatory cell reaction, 50% in the TKI group with moderate inflammatory cell reaction, 35% in the TK+ANT group with moderate inflammatory cell reaction, 75% in the TKI+TCM group with severe inflammatory cell reaction, and the tumor cell necrosis was about 75% in the TKI+TCM group with severe inflammatory cell reaction.In the TKI+PRO group, about 75% of tumor cell necrosis was seen with severe inflammatory response.At bottom, tumor tissues were subjected to immunohistochemical analysis of KI-67 and Caspase-3 (scale bar, 50 µm).The combination of the TCM and gefitinib resulted in decreased KI-67 expression and increased Caspase-3 expression.

Figure S3 .
Figure S3.Comparison of antitumor performance across different treatments (a) The trajectories of tumor volume in different groups (TKI, TKI + ANT, TKI + PRO, TKI + TCM, and TCM alone).(b) The boxplots illustrate the comparison of tumor marker test results (CEA, CYP-19, and NSE) among different groups at the end of the trial.(c) The effect of the Qilian Mixture and gefitinib (both alone and in combination) on PC9 cell proliferation (in vitro).The left and middle panels display the proliferation of PC9 cells when treated with either the Qilian Mixture (0, 250, 500 ng/ml) alone or gefitinib (0, 1, 10 nmol/L) alone.The right panel showcases the proliferation of PC9 cells treated with a combination of the Qilian Mixture (0, 250, 500 ng/ml) and gefitinib (0 or 10 nmol/L).

Figure S4 .
Figure S4.Identification of associated ASVs with antitumor effect of gefitinib.(a) The heat map shows the Spearman correlation between antitumor measurements (e.g., tumor volume, normalized tumor volume, and body weight, measured from day 14 to 35) and the specific gut microbiota.(b) The correlation between tumor marker test results and the gut microbiota at day 35.In particular, 19 ASVs taxonomically annotated as Enterobacter, Klebsiella, Pseudomonas, and Salmonella were found to be positively correlated with tumor volume and negatively correlated with body weight, whereas 22 ASVs belonging to Acetatifactor, Bacteroides, Clostridium, Duncaniella, Kineothrix, Marinilabilia, Muribaculum, Oscillibacter, and Prevotellamassilia were negatively correlated with tumor volume and positively correlated with body weight.In addition to tumor volume and body weight, we calculated the correlations between the tumor marker test results and gut microbiota at the end of the trial (day 35).Specifically, we identified 12 ASVs annotated as Marinilabilia, Muribaculum, Duncaniella, Culturomica, and Mucispirillum that were negatively associated with CEA, CYP-19, and NSE, while 16 ASVs from Marinilabilia, Prevotellamassilia, Duncaniella, Clostridium, Acetatifactor, Bacteroides, Breznakia, Ruminococcus, Muribaculum, and Millionella were positively associated with CEA and NSE.